The pace at which the genetics of disease states are being deciphered has been accelerating. For example, researchers have begun to characterize in detail multiple genetic mechanisms that give rise to cancer, as well as numerous functional pathways associated with cancer such as damage response, cell cycle, cell proliferation and cell death. This exponential growth in our knowledge base of cancer genetics has led to the identification of a large array of genes, proteins and pathways that potentially play a central role in carcinogenesis and/or may be potential targets for therapeutic intervention. The challenge now is to experimentally delve deeper, both into how these genes function and interrelate in vivo and in vitro, as well as into how different compounds and compound classes influence these genes.
Over the last decade, gene expression analysis has proven to be an extremely valuable tool for monitoring the physiologic state of cells and specific pathway responses to different stimuli and environments. This ability to both broadly survey cellular activities and to track differential and dynamic responses suggests that expression tools have been able to provide significant insight into cancer genetics. The current state of the art in large-scale gene expression analysis is the microarray.
Microarrays enable large-scale surveys of thousands of genes for small sets of samples. However, current microarray sample labeling methods are quite costly, e.g. $100 per sample, limiting the number of samples that can be analyzed in microarray format for a given budget. Current labeling methods also require relatively large quantities of RNA (e.g. multiple micrograms of RNA), which limits the types of sources for which RNA analysis can be performed. Several amplification schemes have been developed to compensate for this sample size limitation, including SMART™ technology from BD Clontech (Palo Alto, Calif.), Ovation™ amplification from NuGEN Technologies, Inc. (San Carlos, Calif.), and RiboAmp® RNA amplification kits from Arcturus, Inc. (Mountain View, Calif.), but they all add additional sample handling steps and expense to the reagent cost for each sample. In addition, all of these methods are global amplification schemes that randomly amplify all of the RNA transcripts in the sample. This global amplification, which amplifies all genes (i.e., transcripts) in a sample results in each individual gene being represented in a relatively low ratio relative to all of the remaining amplified transcripts in the sample.
Furthermore, there is a growing trend in gene expression analysis for screening moderate sets of genes, e.g., screening tens to hundreds of genes for hundreds to thousands of samples. For example, to fully capture the activities of functional pathways such as apoptosis or angiogenesis, it is desirable to track between 50 and 100 genes. In fact, linear and nonlinear statistical techniques have been successfully applied to the analysis of microarray data and it is clear that correlation and cluster analysis generally collapses the responses of thousands of genes to a much smaller set of representative genes and response types. For example, Thomas et.al. (Molecular Pharmacology (2001) 60(6):1189-1194) have used this approach to identify 12 key transcripts out of 1200 that can predictively track five major toxicological responses. Also, for example, van't Veer et.al. (Nature (2002) 415:530-536) recently suggested that a set of 70 genes, out of 25,000 tested, could provide a prognostic signature for metastases in breast cancer patients, and that the expression profile outperformed other clinical parameters used to predict disease outcome.
Another major area of interest for a high throughput gene expression assay is compound library screening. The predominant screening assay formats used today fall into two categories: gene specific and phenotypic. Gene-specific screens, such as protein binding assays and reporter gene assays, focus on capturing the effects of a given compound on a single gene or protein endpoint, while phenotypic screens typically capture gross cellular changes such as apoptosis, cell proliferation or ion flux. Both of these screening approaches have significant value, but they are not optimal for screening compounds and how they impact the multiplicity of genes involved in a complex disease like cancer. Gene-specific screens are too focused and cannot observe multigenic responses to perturbations. Cell-based phenotypic screens are too broad and cannot be used to differentiate the multiple pathways that can be altered to produce a phenotypic response nor can they effectively be used to optimize and direct compound development toward specific mechanisms of action. The utilization of a screen that can look at a multiplicity of genes in parallel, e.g. 10-100, can be used to overcome the deficits of these other screening approaches.
While existing microarray methodologies could be pressed into service in these important experimental areas, the fact of the matter is that they cannot not be used because of practical economics associated with the analysis of a large number of samples and minimal quantities of RNA. The present invention addresses these and other concerns as will be apparent upon review of the attached disclosure.